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Open AccessArticle
ADPSCAN: Structural Graph Clustering with Adaptive Density Peak Selection and Noise Re-Clustering
by
Xinyu Du
Xinyu Du
,
Fangfang Li
Fangfang Li *,
Xiaohua Li
Xiaohua Li and
Ge Yu
Ge Yu
School of Computer Science and Engineering, Northeastern University, Shenyang 110169, China
*
Author to whom correspondence should be addressed.
Appl. Sci. 2024, 14(15), 6660; https://doi.org/10.3390/app14156660 (registering DOI)
Submission received: 4 July 2024
/
Revised: 24 July 2024
/
Accepted: 26 July 2024
/
Published: 30 July 2024
Abstract
Structural graph clustering is a data analysis technique that groups nodes within a graph based on their connectivity and structural similarity. The Structural graph clustering SCAN algorithm, a density-based clustering method, effectively identifies core points and their neighbors within areas of high density to form well-defined clusters. However, the clustering quality of SCAN heavily depends on the input parameters, and , making the clustering results highly sensitive to parameter selection. Different parameter settings can lead to significant differences in clustering results, potentially compromising the accuracy of the clusters. To address this issue, a novel structural graph clustering algorithm based on the adaptive selection of density peaks is proposed in this paper. Unlike traditional methods, our algorithm does not rely on external parameters and eliminates the need for manual selection of density peaks or cluster centers by users. Density peaks are adaptively identified using the generalized extreme value distribution, with consideration of the structural similarities and interdependencies among nodes, and clusters are expanded by incorporating neighboring nodes, enhancing the robustness of the clustering process. Additionally, a distance-based structural similarity method is proposed to re-cluster noise nodes to the correct clusters. Extensive experiments on real and synthetic graph datasets validate the effectiveness of our algorithm. The experiment results show that the ADPSCAN has a superior performance compared with several state-of-the-art (SOTA) graph clustering methods.
Share and Cite
MDPI and ACS Style
Du, X.; Li, F.; Li, X.; Yu, G.
ADPSCAN: Structural Graph Clustering with Adaptive Density Peak Selection and Noise Re-Clustering. Appl. Sci. 2024, 14, 6660.
https://doi.org/10.3390/app14156660
AMA Style
Du X, Li F, Li X, Yu G.
ADPSCAN: Structural Graph Clustering with Adaptive Density Peak Selection and Noise Re-Clustering. Applied Sciences. 2024; 14(15):6660.
https://doi.org/10.3390/app14156660
Chicago/Turabian Style
Du, Xinyu, Fangfang Li, Xiaohua Li, and Ge Yu.
2024. "ADPSCAN: Structural Graph Clustering with Adaptive Density Peak Selection and Noise Re-Clustering" Applied Sciences 14, no. 15: 6660.
https://doi.org/10.3390/app14156660
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